突变分析及其工业应用

Rahul Gopinath, Jie M. Zhang, Marinos Kintis, Mike Papadakis
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引用次数: 0

摘要

我们很高兴地向大家介绍《软件测试、验证与测试》杂志突变测试特刊的第二部分。可靠性。由Jong-In Jang、Duksan Ryu和Jongmoon Baik撰写的HOTFUZ:高阶基于突变的故障定位,提出了一种新的基于突变的故障定位(MBFL)成本低的工具HOTFUZ。HOTFUZ使用高阶突变来一次评估多个突变,从而降低了突变测试的成本,这是MBFL技术的一个重大障碍。MuFBDTester:基于突变的测试序列生成器,用于实现核电厂软件的FBD程序,由刘令军、池英英和裴斗焕开发。本文介绍了MuFBDTester,这是一个使用功能框图(FBD)程序的突变测试技术的自动化测试序列生成工具。MuFBDTester将给定的程序和突变体转换为可满足模理论(SMT)求解器的输入语言,以派生出一组测试序列。transut - Spark: Apache Spark的转换突变,作者:Joao Batista de Souza Neto, Anamaria Martins Moreira, Genoveva Vargas-Solar和Martin A. Musicante。本文探讨了突变测试在Apache Spark程序中的应用,提出了用于Apache Spark程序中大数据处理代码自动化突变测试的transut -Spark。Henrique Neves da Silva、Jackson Antonio do Prado Lima、Silvia Regina Vergilio和Andre Takeshi Endo对移动应用的突变测试进行了一项映射研究,根据该领域的趋势和统计数据、研究特征(如重点、建议的运营商和对突变测试阶段的自动化支持)和评估方面分析了16项主要研究。我们感谢编辑、作者和审稿人为成功完成本期特刊所做的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mutation analysis and its industrial applications

We are pleased to introduce the second part of the special issue on Mutation Testing of the Journal of Software Testing, Verification & Reliability.

The issue features the following four papers:
  1. HOTFUZ: cost-effective higher-order mutation-based fault localization by Jong-In Jang, Duksan Ryu, and Jongmoon Baik which proposes HOTFUZ, a novel cost-effective tool for reducing the cost of mutation-based fault localization (MBFL). HOTFUZ uses higher order mutations to evaluate multiple mutations at once which reduces the cost of mutation testing, which is a significant impediment in MBFL techniques.
  2. MuFBDTester: A mutation-based test sequence generator for FBD programs implementing nuclear power plant software by Lingjun Liu, Eunkyoung Jee and Doo-Hwan Bae. This paper presents MuFBDTester, an automated test sequence generation tool using mutation testing techniques for Function Block Diagram (FBD) programs. MuFBDTester translates the given program and mutants into the input language of a Satisfiability Modulo Theories (SMT) solver to derive a set of test sequences.
  3. TRANSMUT–Spark: Transformation Mutation for Apache Spark by Joao Batista de Souza Neto, Anamaria Martins Moreira, Genoveva Vargas-Solar, and Martin A. Musicante. The paper explores the application of mutation testing in Apache Spark programs, and proposes TRANSMUT-Spark for automating mutation testing of Big Data processing code within Apache Spark programs.
  4. A mapping study on mutation testing for mobile applications by Henrique Neves da Silva, Jackson Antonio do Prado Lima, Silvia Regina Vergilio and Andre Takeshi Endo, which analyzed 16 primary studies according to the trends and statistics about the field, the study characteristics (such as focus, proposed operators, and automated support for the mutation testing phases), and the evaluation aspects.

We thank the editors, authors, and reviewers for their efforts to successfully complete this special issue.

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